Online shopping: How can algorithm performance expectancy enhance impulse buying?
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When users believe the algorithm is capable of delivering highly relevant suggestions, they are more inclined to trust the recommendations.
“When users hold higher expectations regarding the performance and effectiveness of algorithmic recommendations-namely, when they believe the algorithm is capable of delivering highly relevant suggestions-they are more inclined to trust the recommendations.”
Perceived trust reduces cognitive resistance and perceived risk, thereby increasing users' willingness to adopt recommended products.
“This heightened trust, in turn, reduces cognitive resistance and perceived risk, thereby increasing users' willingness to adopt the products or services recommended by the algorithm [47].”
Cites
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The Persuasion Knowledge Model: How People Cope with Persuasion Attempts
Friestad, M., & Wright, P. (1994) · Journal of Consumer Research · doi:10.1086/209380 · Cited by 3,811 · see citing works
Normative Influences on Impulsive Buying Behavior
Rook, D. W., & Fisher, R. J. (1995) · Journal of Consumer Research · doi:10.1086/209452 · Cited by 1,947 · see citing works
The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374 · see citing works
Data Privacy: Effects on Customer and Firm Performance
Martin, K. D., Borah, A., & Palmatier, R. W. (2017) · Journal of Marketing · doi:10.1509/jm.15.0497 · Cited by 786 · see citing works
Chattaraman, V., Kwon, W.-S., Gilbert, J. E., & Ross, K. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.048 · Cited by 510 · see citing works
Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018) · Information & Management · doi:10.1016/j.im.2018.04.010 · Cited by 477 · see citing works
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Yang, F., Tang, J., Men, J., & Zheng, X. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102683 · Cited by 235 · see citing works
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Hu, S., Liu, J., Li, H., Yin, J., & Liu, X. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20040290 · Cited by 11 · see citing works
- Authors
- Gallin, S., & Portes, A.
- Affiliations
- MBS School of Business (FR)
- Corresponding author
- Gallin, Steffie
- Venue
- Journal of Retailing and Consumer Services
- Publisher
- Elsevier BV
- Published
- 2024-11
- Volume
- 81
- Pages
- 103988
- ISSN
- 0969-6989 (ISSN-L)
- OpenAlex
- W4400398676
- Cited by
- 41
- References
- 9
- Citations by year
- 2026: 19 · 2025: 19 · 2024: 3
- FWCI
- 18.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Retailing and Consumer Services (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Retail Behavior Studies
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- online shopping, impulse buying, trust in algorithms, product recommendation, personalized recommendation, online retail, consumer behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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